math benchmark
FinQA
A large-scale dataset for numerical reasoning over financial data with question-answering pairs written by financial experts, featuring complex numerical reasoning and understanding of heterogeneous representations with annotated gold reasoning programs for full explainability
Updated Aug 11, 2026
Models3
Model coverage3
MetricScore
EvidenceB
FinQA Ranking
Higher score ranks better on this benchmark.
| 1 | AM Nova ProAmazon | 77.2% | 100.0% | 3 | C | |
| 2 | AMNova LiteAmazon | 73.6% | 50.0% | 3 | C | |
| 3 | AMNova MicroAmazon | 65.2% | 0.0% | 3 | C |
FinQA Score Distribution
A closer view of the leading scores on this benchmark.
FinQA Highlights
The leading models and scores on this benchmark.
What is FinQA?
What FinQA measures and how its scores work.
A large-scale dataset for numerical reasoning over financial data with question-answering pairs written by financial experts, featuring complex numerical reasoning and understanding of heterogeneous representations with annotated gold reasoning programs for full explainability
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- FinQA
- Modality
- text
- Primary category
- math
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- finqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about FinQA.
Which model scores highest on FinQA?
Nova Pro is currently ranked first with 77.2%.
What does FinQA measure?
A large-scale dataset for numerical reasoning over financial data with question-answering pairs written by financial experts, featuring complex numerical reasoning and understanding of heterogeneous representations with annotated gold reasoning programs for full explainability
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.